The brand of cloud platform used for data storage

The operating system installed on the gateway device

The colour and size of the sensor housing

The distance between sensors and gateways, combined with whether the sensors are battery-powered or mains-powered

It reduces data volume to manageable levels and smooths out short-term noise to reveal underlying conditions

It converts raw readings into regulatory compliance reports and archives original data

It encrypts sensor data for secure transmission and validates sensor calibration

It increases the accuracy of individual readings and creates backup copies of the data

Environmental regulations prohibit sensors from using cellular frequencies

Direct cellular connections would require each sensor to have a cellular radio, significantly increasing power consumption and reducing battery life

Cellular networks do not support environmental data transmission formats

Short-range protocols transmit data faster than cellular connections

The combination eliminates the need for data cleaning because satellite data is already validated

Satellite data covers large geographic areas while ground sensors provide accurate calibration points at specific locations, producing more reliable results than either source independently

Satellite data replaces the need for ground sensors entirely, reducing hardware costs

Ground sensors provide global coverage while satellites provide accuracy at individual measurement points

It receives raw readings, removes invalid entries, normalises timestamps, and stores cleaned records for analysis

It compares aggregated values against regulatory thresholds and triggers notifications

It measures physical parameters such as temperature, gas concentration, and water pH

It generates alerts and compliance reports for regulators and operators

Option B, because the hydro region has the lowest absolute intensity

Option A, because the solar region’s large intensity variation creates a meaningful clean window, the job is flexible with deadline slack, and no data transfer is required

All three options are equally feasible because they all target carbon reduction

Option C, because the matching algorithm consumes the most energy and the hydro region has the lowest intensity

Inventory reconciliation, because its 2-hour SLA and 1-hour job duration leave only 1 hour of scheduling slack

Sales analytics, because it runs overnight when carbon intensity is already low

Recommendation retraining, because machine learning jobs are too computationally expensive to defer

Order processing, because it is an interactive foreground workload that cannot be deferred at all

Temporal shifting within Region B after first transferring all videos there permanently

Temporal shifting within Region A, because avoiding data transfer overhead is always preferable

Neither strategy, because video encoding is an inflexible workload that cannot be shifted

Spatial shifting to Region B, because Region A’s flat intensity profile means temporal shifting offers almost no benefit, while Region B has substantially lower intensity

Organisation A, because the fixed engineering cost is spread across a much larger volume of flexible compute, producing substantially more total carbon savings per unit of investment

Both organisations equally, because the engineering effort is the same

Organisation B, because smaller organisations have a greater responsibility to reduce emissions

Neither organisation, because carbon-aware scheduling only works for hyperscale cloud providers

Start the job by 22:00 the previous evening regardless of carbon intensity, because waiting for the 01:00 clean window would miss the 08:00 deadline

Cancel the job because no clean window can accommodate 10 hours

Split the job into two 5-hour segments and run each in a separate clean window

Extend the deadline to 12:00 to allow the job to run entirely within a clean window